Recommendations
Books, Blogs, and other materials
I was thinking the other day about useful sources of information for modern ML Engineering and Architecture (not data science/algorithm development) - and came up with the following. This is orientated for High Energy Physics academics (as that’s my experience). For posterity (and we will see how this ages), here goes:
I will write some justifications another time :) on a more detailed basis!
Key learning goals
- Be familiar with technical systems in business environments
- Be able to express physics experience in business concepts
- Be able to understand the environment you will be working in
Data Science Concepts to learn:
- Basic econometrics (stats + economics)
- Survival analysis and more biology concepts
- A/B testing
- Dashboards & Viz technologies
Tech Books:
- Designing Data Intensive Applications
- High Performance Python
- Spark : The Definitive Guide
- Streaming Systems
- Machine Learning Design Patterns
- Building Machine Learning Powered Applications
- Site Reliability Engineering
Cloud
Potentially get yourself a cloud certification
- Google Cloud Engineer - this will help you understand the cloud for business more
Newsletters:
Business concepts:
- Project Management - Agile / Waterfall
- People Management - teams
- How to run a business - profits, costs, basic finances for a business
Business books:
- The Manager’s Path (Fornier)
- The Making of a Manager (Zhao)
- The Answers: - Lucy Kellaway - valuable culture information
- The Lean Startup:
- Managing the Professional Services firm (the business of consulting) - Meister
- Who Moved My Blackberry - business culture
- The Money Machine - how the city works
- Quiet - Susan Cain (introverts)
- Barbarians at the Gate - old style M&A
- Microserfs - old school silicon valley